Standard deep learning models have been shown to be poorly calibrated [1]}. This should be concerning for safety-critical applications including medicine. A range of methods have been proposed for improving the calibration of deep learning models. For example, it has been shown that calibration can be improved by using a proper scoring rule as the loss function [2]}, [1]}, using weight decay and avoiding batch normalization [1]}. Training on adversarial examples [5]} has also been shown to improve model calibration [2]}. Some studies have used the Platt scaling for improving the model calibration [7]}, [1]}. In [9]}, for instance, after the deep learning network is trained, a model \(q= \text{Softmax}(a z + b)\)  with parameters \(a\)  and \(b\)  is trained on the logit vector, \(z\) , of the trained network to obtain a more calibrated prediction \(q\) . Another study proposed to train a separate model, to map the uncalibrated output of a CNN to calibrated probabilities [10]}. For this purpose, they used a Bayesian neural network, which they trained after training the main deep learning model.
